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Conference Paper
Exploration of Lossy Posture Classification Model using in-Bed Flexible Pressure Sensors
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- Authors
- Aekyeung Moon, Seung Woo Son, Minjun Kim, Seyun Chang, Hyeji Park
- Issue Date
- 2023-07
- Citation
- International Conference on Flexible and Printable Sensors and Systems (FLEPS) 2023, pp.1-4
- Language
- English
- Type
- Conference Paper
- Abstract
- Advances in flexible and printable sensor technologies have made it possible to use posture classification for providing timely services in digital healthcare, especially for bedsores or decubitus ulcers. However, managing a large amount of sensor data and ensuring accurate predictions can be challenging. While lossy compressors can reduce data volume, it is still unclear whether this would lead to losing important information and affect downstream application performance. In this paper, we propose LCDNN (Lossy Compression using Deep Neural Network) to reduce the size of sensor data and evaluate the performance of posture classification models. Our sensors, placed under hospital beds, have a thickness of just 0.4mm and collect pressure data from 28 sensors (7 by 4) at an 8 Hz cycle, categorizing postures into 4 types from 5 patients. Our evaluation, which includes reduced datasets by LCDNN, demonstrates that the results are promising.
- KSP Keywords
- Classification models, Data Volume, Deep neural network(DNN), Digital healthcare, Flexible pressure sensor, Hospital beds, Lossy Compression, Sensor Technology, application performance, posture classification, printable sensor